May 2022 arXiv papers — page 12

Bruno L. M. Ferreira, Aisha Jabeen

Let $\mathfrak{R}$ and $\mathfrak{R}'$ be two associative rings (not necessarily with the identity elements). A bijective map $\varphi$ of $\mathfrak{R}$ onto $\mathfrak{R}'$ is called a \textit{$m$-multiplicative isomorphism} if {$\varphi (x_{1} \cdots x_{m}) = \varphi(x_{1}) \cdots \varphi(x_{m})$} for all $x_{1}, \cdots ,x_{m}\in \mathfrak{R}.$ In this ar

Chen Li, Antonios Tsourdos, Weisi Guo

Deep Learning (DL) has transformed the automation of a wide range of industries and finds increasing ubiquity in society. The high complexity of DL models and its widespread adoption has led to global energy consumption doubling every 3-4 months. Currently, the relationship between the DL model configuration and energy consumption is not well established. At

J. Yu. Panteleeva, E. Epelbaum, J. Gegelia, U. -G. Meißner

An unambiguous definition of the electromagnetic spatial densities for a spin-1/2 system is proposed and worked out in the zero average momentum frame and in moving frames. The obtained results are compared with the traditional definition of the densities in terms of the three-dimensional Fourier transforms of the electromagnetic form factors in the Breit fr

Ting-En Lin, Yuchuan Wu, Fei Huang, Luo Si

In this paper, we present Duplex Conversation, a multi-turn, multimodal spoken dialogue system that enables telephone-based agents to interact with customers like a human. We use the concept of full-duplex in telecommunication to demonstrate what a human-like interactive experience should be and how to achieve smooth turn-taking through three subtasks: user

Tao Li, Cheng Meng, Hongteng Xu, Jun Yu

Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling. In this study, we propose a novel metric, called Hilbert curve projection (HCP) distance, to measure the distance between two probability distributions with low complexity. In particular, we first project two high-dimensional probabil

Derk P. Kooi

We introduce 1-RDMFT in the canonical ensemble and then proceed to approximate the interacting ensemble by a non-interacting ensemble that maximizes the entropy, independently of temperature. Bosonic and Fermionic Sinkhorn algorithms are derived and used to invert the relationship between the Natural Orbital Occupation Numbers (NOONs) and the effective orbit

Jian-Jian Miao, Hui-Ke Jin, Yi Zhou

Starting from the Landau's kinetic equation, we show that an electronic liquid in $d=2,3$ dimensions depicted by a Landau type effective theory will become incompressible on condition that the Landau parameters satisfy either (i) $1+F_{1}^{s}/d=0$ or (ii) $F_{0}^{s}\to{}+\infty$. The condition (i) is the Pomeranchuk instability in the current channel and sug

Huifang Huang, Ting Gao, Yi Gui, Jin Guo

Reinforcement learning (RL) is gaining attention by more and more researchers in quantitative finance as the agent-environment interaction framework is aligned with decision making process in many business problems. Most of the current financial applications using RL algorithms are based on model-free method, which still faces stability and adaptivity challe

Zhijie Chen, Houwang Li, Wenming Zou

We study the Lane-Emden system $$\begin{cases} -\Delta u=v^p,\quad u>0,\quad\text{in}~\Omega, -\Delta v=u^q,\quad v>0,\quad\text{in}~\Omega, u=v=0,\quad\text{on}~\partial\Omega, \end{cases}$$ where $\Omega\subset\mathbb{R}^2$ is a smooth bounded domain. In a recent work, we studied the concentration phenomena of positive solutions as $p,q\to+\infty$ and $|q-

Yacin Ameur, Felipe Marceca, José Luis Romero

The Gaussian $\beta$-ensemble is a real $n$-point configuration $\{x_j\}_1^n$ picked randomly with respect to the Boltzmann factor $e^{-\frac\beta 2H_n}$, $H_n=\sum_{i\ne j}\log\frac 1{|x_i-x_j|}+n\sum_{i=1}^n\tfrac 12x_i^2.$ The point process $\{x_j\}_1^n$ tends to follow the semicircle law $\sigma(x)=\tfrac 1{2\pi}\sqrt{(4-x^2)_+}$ in certain average sense

Thomas Germer, Tobias Uelwer, Stefan Harmeling

In this paper, we present our approach for the Helsinki Deblur Challenge (HDC2021). The task of this challenge is to deblur images of characters without knowing the point spread function (PSF). The organizers provided a dataset of pairs of sharp and blurred images. Our method consists of three steps: First, we estimate a warping transformation of the images

Fatima Ezzahra Airod, Mattia Merluzzi, Paolo Di Lorenzo, Emilio Calvanese Strinati

The advent of Reconfigurable Intelligent Surfaces (RISs) in wireless communication networks unlocks the way to support high frequency radio access (e.g. in millimeter wave) while overcoming their sensitivity to the presence of deep fading and blockages. In support of this vision, this work exhibits the forward-looking perception of using RIS to enhance the c

Sotaro Takeshita, Tommaso Green, Niklas Friedrich, Kai Eckert

The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Consequently, recent work on applying text mining technologies for scholarly publications has investigated the application of automatic text summarization

Steffen W. R. Werner, Michael L. Overton, Benjamin Peherstorfer

Robust controllers that stabilize dynamical systems even under disturbances and noise are often formulated as solutions of nonsmooth, nonconvex optimization problems. While methods such as gradient sampling can handle the nonconvexity and nonsmoothness, the costs of evaluating the objective function may be substantial, making robust control challenging for d

Yves Rychener, Bahar Taskesen, Daniel Kuhn

We study supervised learning problems that have significant effects on individuals from two demographic groups, and we seek predictors that are fair with respect to a group fairness criterion such as statistical parity (SP). A predictor is SP-fair if the distributions of predictions within the two groups are close in Kolmogorov distance, and fairness is achi

Paolo Leonetti, Cihan Orhan

We provide necessary and/or sufficient conditions on vector spaces $V$ of real sequences to be a Fr\'{e}chet space such that each coordinate map is continuous, that is, to be a locally convex FK space. In particular, we show that if $c_{00}(\mathcal{I})\subseteq V\subseteq \ell_\infty(\mathcal{I})$ for some ideal $\mathcal{I}$ on $\omega$, then $V$ is a loca

Klaudia-Doris Thellmann, Bernhard Stadler, Ricardo Usbeck, Jens Lehmann

While a considerable amount of semantic parsing approaches have employed RNN architectures for code generation tasks, there have been only few attempts to investigate the applicability of Transformers for this task. Including hierarchical information of the underlying programming language syntax has proven to be effective for code generation. Since the posit

S. Anishchenko, P. Bogdanovich, A. Gurinovich, D. Leonenko

Design of helical flux compression generator with double-end initiation is described. The developed design approach makes it possible to increase the power in the load by 60\% and shorten the rise time of the pulse by 83\% in comparison with a conventional helical FCG that is made in the same dimensions but with the single-end initiation of explosive. When u

A. Bonhomme, C. Buck, B. Gramlich, M. Raab

Many experiments in particle physics, in particular in the field of neutrino searches, rely on organic liquid scintillators as target and detection material. The size of these detectors was continously growing in the last decades, up to the kiloton scale. In several cases these detectors are located at sites with enhanced safety requirements as underground l

Hao Wang, Ziyu Zhan, Futai Hu, Yuan Meng

Orbital angular momentum (OAM) detection underpins almost all aspects of vortex beams' advances such as communication and quantum analogy. Conventional schemes are frustrated by low speed, complicated system, limited detection range. Here, we devise an intelligent processor composed of photonic and electronic neurons for OAM spectrum measurement in a fast, a

Yiqin Tan, Pihe Hu, Ling Pan, Jiatai Huang

Training deep reinforcement learning (DRL) models usually requires high computation costs. Therefore, compressing DRL models possesses immense potential for training acceleration and model deployment. However, existing methods that generate small models mainly adopt the knowledge distillation-based approach by iteratively training a dense network. As a resul

Erez Michaely, Smadar Naoz

The third observing run of the LIGO/Virgo/KARGA collaboration reported a few neutron star - black hole (NSBH) merger events. While NSBH mergers have yet to receive extensive theoretical attention, they may have a promising electromagnetic signature in the form of short gamma - ray bursts. Here we show that NSBH dynamical mergers can naturally form from ultra

Pierre Bras, Gilles Pagès

We study the convergence of Langevin-Simulated Annealing type algorithms with multiplicative noise, i.e. for $V : \mathbb{R}^d \to \mathbb{R}$ a potential function to minimize, we consider the stochastic differential equation $dY_t = - \sigma \sigma^\top \nabla V(Y_t) dt + a(t)\sigma(Y_t)dW_t + a(t)^2\Upsilon(Y_t)dt$, where $(W_t)$ is a Brownian motion, wher

Gargi Mitra, Prasanna Karthik Vairam, Sandip Saha, Nitin Chandrachoodan

Internet users are vulnerable to privacy attacks despite the use of encryption. Webpage fingerprinting, an attack that analyzes encrypted traffic, can identify the webpages visited by a user in a given website. Recent research works have been successful in demonstrating webpage fingerprinting attacks on individual users, but have been unsuccessful in extendi

Zur Izhakian, Manfred Knebusch

Classes of an equivalence relation on a module V over a supertropical semiring, called rays, carry the underlaying structure of "supertropical trigonometry" and thereby a version of convex geometry which is compatible with quasilinearity. In this theory the traditional Cauchy-Schwarz inequality is replaced by the CS-ratio which gives rise to special characte

Shilpa Rana, Mrutunjaya Bhuyan, Raj Kumar

Background: The nuclear interaction potential and hence the fusion barrier formed between the interacting nuclei are the keys to understanding the complex fusion process dynamics. Purpose: This work intends to explore the fusion barrier characteristics of different target-projectile combinations within the relativistic mean-field (RMF) formalism. Methods: Th

Shuwei Shao, Zhongcai Pei, Weihai Chen, Xingming Wu

This work delves into unsupervised monocular depth estimation in endoscopy, which leverages adjacent frames to establish a supervisory signal during the training phase. For many clinical applications, e.g., surgical navigation, temporally correlated frames are also available at test time. Due to the lack of depth clues, making full use of the temporal correl

Baptiste Goujaud, Adrien Taylor, Aymeric Dieuleveut

We analyze worst-case convergence guarantees of first-order optimization methods over a function class extending that of smooth and convex functions. This class contains convex functions that admit a simple quadratic upper bound. Its study is motivated by its stability under minor perturbations. We provide a thorough analysis of first-order methods, includin

Marcin Gąsiorek

A poset $I=(\{1,\ldots, n\}, \leq_I)$ is called non-negative if the symmetric Gram matrix $G_I:=\frac{1}{2}(C_I + C_I^{tr})\in\mathbb{M}_n(\mathbb{R})$ is positive semi-definite, where $C_I\in\mathbb{M}_n(\mathbb{Z})$ is the $(0,1)$-matrix encoding the relation $\leq_I$. Every such a connected poset $I$, up to the $\mathbb{Z}$-congruence of the $G_I$ matrix,

Tobias Kallehauge, Pablo Ramìrez-Espinosa, Anders E. Kalør, Christophe Biscio

This paper proposes exploiting the spatial correlation of wireless channel statistics beyond the conventional received signal strength maps by constructing statistical radio maps to predict any relevant channel statistics to assist communications. Specifically, from stored channel samples acquired by previous users in the network, we use Gaussian processes (

Juan Calderon Bustillo, Isaac C. F. Wong, Nicolas Sanchis-Gual, Samson H. W. Leong

Detection and parameter inference of gravitational-wave signals \ncor{from compact mergers} rely on the comparison of the incoming detector strain data $d(t)$ to waveform templates for the gravitational-wave strain $h(t)$ that ultimately rely on the resolution of Einstein's equations via numerical relativity simulations. These, however, commonly output a qua

Erik Skibsted

Within the class of Derezi{\'n}ski-Enss pair-potentials which includes Coulomb potentials a stationary scattering theory for $N$-body systems was recently developed \cite {Sk1}. In particular the wave and scattering matrices as well as the restricted wave operators are all defined at any non-threshold energy, and this holds without imposing any a priori deca

Aditya Kane, Sahil Khose

Designing efficient and reliable VQA systems remains a challenging problem, more so in the case of disaster management and response systems. In this work, we revisit fundamental combination methods like concatenation, addition and element-wise multiplication with modern image and text feature abstraction models. We design a simple and efficient system which

Benjamin Scellier, Siddhartha Mishra, Yoshua Bengio, Yann Ollivier

This work establishes that a physical system can perform statistical learning without gradient computations, via an Agnostic Equilibrium Propagation (Aeqprop) procedure that combines energy minimization, homeostatic control, and nudging towards the correct response. In Aeqprop, the specifics of the system do not have to be known: the procedure is based only

Amir Saki, Usef Faghihi

In this paper, we introduce a fundamental framework to create a bridge between Probability Theory and Fuzzy Logic. Indeed, our theory formulates a random experiment of selecting crisp elements with the criterion of having a certain fuzzy attribute. To do so, we associate some specific crisp random variables to the random experiment. Then, several formulas ar

Hadrien Hendrikx

We consider a decentralized optimization problem, in which $n$ nodes collaborate to optimize a global objective function using local communications only. While many decentralized algorithms focus on \emph{gossip} communications (pairwise averaging), we consider a different scheme, in which a ``token'' that contains the current estimate of the model performs

Zaiyun Yang

Transductive methods always outperform inductive methods in few-shot image classification scenarios. However, the existing few-shot methods contain a latent condition: the number of samples in each class is the same, which may be unrealistic. To cope with those cases where the query shots of each class are nonuniform (i.e. nonuniform few-shot learning), we p

M. J. Kronenburg

Using $P(n,m)$, the number of integer partitions of $n$ into exactly $m$ parts, which was the subject of an earlier paper, $P(n,m,p)$, the number of integer partitions of $n$ into exactly $m$ parts with each part at most $p$, can be computed in $O(n^2)$, and the q-binomial coefficient can be computed in $O(n^3)$. Using the definition of the q-binomial coeffi

Tiankai Zhao, Hongyan Yuan

The reaction-diffusion models have been extensively applied to explain the mechanism of pattern formations in early embryogenesis based on geometrically confined microtissues consisting of human pluripotent stem cells. Recently, mechanical cues, such as the cellular stresses and strains, have been found to dictate the pattern formation in human stem cell dif

Udai Prakash Tyagi, Kakoli Bera, Partha Goswami

We study the emergence of the quantum spin Hall (QSH) states for the pseudo-gap (PG) phase of Bi2212 bilayer system, assumed to be D-density wave(DDW) ordered, starting with a strong Rashba spin-orbit coupling(SOC) armed, and the time reversal symmetry (TRS) complaint Bloch Hamiltonian. The presence of strong SOC gives rise to non-trivial, spin-momentum lock

Moad Abudia, Joel A. Rosenfeld, Rushikesh Kamalapurkar

This paper concerns identification of uncontrolled or closed loop nonlinear systems using a set of trajectories that are generated by the system in a domain of attraction. The objective is to ensure that the trajectories of the identified systems are close to the trajectories of the real system, as quantified by an error bound that is prescribed a priori. A

Md. Manirul Ali

Bell's theorem revealed that a local hidden-variable model cannot completely reproduce the quantum mechanical predictions. Bell's inequality provides an upper bound under the locality and reality assumptions that can be violated by correlated measurement statistics of quantum mechanics. Greenberger, Horne, and Zeilinger (GHZ) gave a more compelling proof of

Juan Maroñas, Daniel Hernández-Lobato

This work introduces the Efficient Transformed Gaussian Process (ETGP), a new way of creating C stochastic processes characterized by: 1) the C processes are non-stationary, 2) the C processes are dependent by construction without needing a mixing matrix, 3) training and making predictions is very efficient since the number of Gaussian Processes (GP) operati

Thomas Bothner

We characterize Fredholm determinants of a class of Hankel composition operators via matrix-valued Riemann-Hilbert problems, for additive and multiplicative compositions. The scalar-valued kernels of the underlying integral operators are not assumed to display the integrable structure known from the seminal work of Its, Izergin, Korepin and Slavnov \cite{IIK

Salim Janji, Adam Samorzewski, Małgorzata Wasilewska, Adrian Kliks

We consider free-space optical (FSO) communication links for the backhaul connectivity of small cells (SCs) where a UAV with an FSO apparatus can serve as a backhaul relay node. We demonstrate how such drone relay stations (DRSs) can be deployed in a high-rise urban area in order to provide FSO line-of-sight (LOS) links that are unobstructed by buildings. Al

Victor Lekeu, Yi Zhang

Using the Atiyah-Singer index theorem, we formally compute gravitational anomalies for fermionic higher-spin fields in two, six and ten dimensions, as well as the U(1) mixed gauge-gravitational anomaly in four dimensions. In all cases, anomaly cancellations are found for an infinite tower of fields with alternating chiralities.

Valerii Likhosherstov, Krzysztof Choromanski, Avinava Dubey, Frederick Liu

We introduce chefs' random tables (CRTs), a new class of non-trigonometric random features (RFs) to approximate Gaussian and softmax kernels. CRTs are an alternative to standard random kitchen sink (RKS) methods, which inherently rely on the trigonometric maps. We present variants of CRTs where RFs are positive, a key requirement for applications in recent l

Zhongyu Wan, Wenjun Xu, Tianyi Yang, Ruiqin Zhang

Inorganic electrides are a new class of compounds catering to the interest of scientists due to the multiple usages exhibited by interstitial electrons in the lattice. However, the influence of the shape and distribution of interstitial electrons on physical properties and new forms of physical states are still unknown. In this work, crystal structure search

Su Yeon Chang, Edwin Agnew, Elías F. Combarro, Michele Grossi

In an earlier work, we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of a quantum GAN. We applied the model on a realistic High-Energy Physics (HEP) use case: the exact theoretical simulation of a calorimeter response with a reduced problem size. This paper explores the dual- PQC GAN for a mo

Zhongyu Wan, Chao Zhang, Tianyi Yang, Wenjun Xu

Recently, electrides have received increasing attention due to their multifunctional properties as superconducting, catalytic, insulating, and electrode materials, with potential to offer other performance and possess novel physical states. This work uncovers that Li$_5$N as an electride possess four novel physical states simultaneously: electride state, sup

Tingyan Kuang, Huichao Chen, Lu Han, Rong He

With the increasingly complex and changeable electromagnetic environment, wireless communication systems are facing jamming and abnormal signal injection, which significantly affects the normal operation of a communication system. In particular, the abnormal signals may emulate the normal signals, which makes it very challenging for abnormal signal recogniti

P. Holmvall, N. Wall Wennerdal, M. Håkansson, P. Stadler

We present SuperConga, an open-source framework for simulating equilibrium properties of unconventional and ballistic singlet superconductors, confined to two-dimensional (2D) mesoscopic grains in a perpendicular external magnetic field, at arbitrary low temperature. It aims at being both fast and easy to use, enabling research without access to a computer c

Jun Li, Shangwei Guo, Shaokun Han

Point cloud completion task aims to predict the missing part of incomplete point clouds and generate complete point clouds with details. In this paper, we propose a novel point cloud completion network, namely CompleteDT. Specifically, features are learned from point clouds with different resolutions, which is sampled from the incomplete input, and are conve

Maik Punke, Steven M. Wise, Axel Voigt, Marco Salvalaglio

We present a phase-field crystal (PFC) model for solidification that accounts for thermal transport and a temperature-dependent lattice parameter. Elasticity effects are characterized through the continuous elastic field computed from the microscopic density field. We showcase the model capabilities via selected numerical investigations which focus on the pr

Ekaterina I. Marchenko, Artem R. Oganov, Efim A. Mazhnik, Nikolay N. Eremin

Using evolutionary crystal structure prediction algorithm USPEX, we showed that at pressures of the Earth's lower mantle CaAl2O4 is the only stable calcium aluminate. At pressures above 7.0 GPa it has the CaFe2O4-type structure and space group Pnma. This phase is one of prime candidate aluminous phases in the lower mantle of the Earth. We show that at low pr

Satyaki Bhattacharya, Stanislav Volkov

We introduce the notion of {\bf a}-walk $S(n)=a_1 X_1+\dots+a_n X_n$, based on a sequence of positive numbers ${\bf a}=(a_1,a_2,\dots)$ and a Rademacher sequence $X_1,X_2,\dots$. We study recurrence/transience (properly defined) of such walks for various sequences of ${\bf a}$. In particular, we establish the classification in the cases where $a_k=\lfloor k^

Jeppe Ekstrand Halkjær Madsen, Thomas Scheike, Christian Pipper

We introduce causal inference reasoning to cross-over trials, with a focus on Thorough QT (TQT) studies. For such trials, we propose different sets of assumptions and consider their impact on the modelling strategy and estimation procedure. We show that unbiased estimates of a causal treatment effect are obtained by a G-computation approach in combination wi

Zishu Zhan, Xiangjie Li, Jingxiao Zhang

Missing data is a common problem in clinical data collection, which causes difficulty in the statistical analysis of such data. In this article, we consider the problem under a framework of a semiparametric partially linear model when observations are subject to missingness with complex patterns. If the correct model structure of the additive partially linea

Gabriel F. Rodríguez Ruiz, Michael A. Rampp, A. A. Aligia, Joerg Schmalian

We determine the current-phase relation (CPR) of two-terminal configurations of Josephson junctions containing two-dimensional (2D) time-reversal invariant topological superconductors (TRITOPS), including TRITOPS-TRITOPS, as well as junctions between topological and non-topological superconductors (TRITOPS-S). We focus on long junctions for which several cha

Edgars Butanovs, Alexei Kuzmin, Aleksejs Zolotarjovs, Sergei Vlassov

During the synthesis of heterostructured nanomaterials, unwanted structural and morphological changes in nanostructures may occur, especially when multiple sequential growth steps are involved. In this study, we describe a synthesis strategy of heterostructured ZnS/Al2O3/MoS2 core-shell nanowires (NWs), and explore the role of the Al2O3 interlayer during syn

Edgars Butanovs, Kevon Kadiwala, Aleksejs Gopejenko, Dmitry Bocharov

One-dimensional (1D) nanostructures - nanowires (NWs) - exhibit promising properties for integration in different types of functional devices. Their properties can be enhanced even further or tuned for a specific application by combining different promising materials, such as layered van der Waals materials and conventional semiconductors, into 1D-1D core-sh

Vadim Malyshev, Mikhail Menshikov, Serguei Popov, Andrew Wade

We study finite particle systems on the one-dimensional integer lattice, where each particle performs a continuous-time nearest-neighbour random walk, with jump rates intrinsic to each particle, subject to an exclusion interaction which suppresses jumps that would lead to more than one particle occupying any site. We show that the particle jump rates determi

Samuel Coward, George A. Constantinides, Theo Drane

E-graphs are a data structure that compactly represents equivalent expressions. They are constructed via the repeated application of rewrite rules. Often in practical applications, conditional rewrite rules are crucial, but their application requires the detection - at the time the e-graph is being built - that a condition is valid in the domain of applicati

Zongqi Wan, Zhijie Zhang, Tongyang Li, Jialin Zhang

Multi-arm bandit (MAB) and stochastic linear bandit (SLB) are important models in reinforcement learning, and it is well-known that classical algorithms for bandits with time horizon $T$ suffer $\Omega(\sqrt{T})$ regret. In this paper, we study MAB and SLB with quantum reward oracles and propose quantum algorithms for both models with $O(\mbox{poly}(\log T))

Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth

We provide the first complete continuous time framework for denoising diffusion models of discrete data. This is achieved by formulating the forward noising process and corresponding reverse time generative process as Continuous Time Markov Chains (CTMCs). The model can be efficiently trained using a continuous time version of the ELBO. We simulate the high

Sara Atito, Muhammad Awais, Josef Kittler

Vision transformers have generated significant interest in the computer vision community because of their flexibility in exploiting contextual information, whether it is sharply confined local, or long range global. However, they are known to be data hungry. This has motivated the research in self-supervised transformer pretraining, which does not need to de

Marilena Vivona, Gabriele Bellocchi, Raffaella Lo Nigro, Simone Rascuná

In this paper, we investigate the electrical evolution of tungsten (W) and tungsten carbide (WC) Schottky contacts on 4H-SiC subjected to thermal treatments at different annealing temperatures from 475 to 700 {\deg} C. For each annealing temperature, the uniformity of the Schottky barrier height (${\phi_B}$) and ideality factor (n) was monitored by current-v

Andrea Lucchini, Pablo Spiga

Given a finite group $G$, the Engel graph of $G$ is a directed graph encoding pairs of elements satisfying some Engel word. From the work of Detomi, Lucchini and Nemmi, the strongly connectivity of the Engel graph of an arbitrary group $G$ is reduced to the understanding of the strongly connectivity of the Engel graph of non-abelian simple groups. In this pa

Saiyad Ashanujjaman, Kirtiman Ghosh, Katri Huitu

While the triplet-like Higgses up to a few hundred GeV masses are already excluded for a vast region of the model parameter space from the LHC searches, strikingly, there is a region of this parameter space that is beyond the reach of the existing LHC searches, and doubly/singly-charged and neutral Higgses as light as 200 GeV or even lighter are still allowe

Debasmita Mukherjee, Sishu Shankar Muni, Hammed Olawale Fatoyinbo

Atherosclerosis is a chronic inflammatory disease occurs due to plaque accumulation in the inner artery wall. In atherosclerotic plaque formation monocytes and macrophages play a significant role in controlling the disease dynamics. In the present article, the entire biochemical process of atherosclerotic plaque formation is presented in terms of an autonomo

Chia-Chien Hung, Tommaso Green, Robert Litschko, Tornike Tsereteli

This paper introduces our proposed system for the MIA Shared Task on Cross-lingual Open-retrieval Question Answering (COQA). In this challenging scenario, given an input question the system has to gather evidence documents from a multilingual pool and generate from them an answer in the language of the question. We devised several approaches combining differ

Waldemar Svejstrup, Andrea Maiani, Kevin Van Hoogdalem, Karsten Flensberg

The route to reliable quantum nanoelectronic devices hinges on precise control of the electrostatic environment. For this reason, accurate methods for electrostatic simulations are essential in the design process. The most widespread methods for this purpose are the Thomas-Fermi approximation, which provides quick approximate results, and the Schr\"odinger-P

Arata Komyo

In this paper, a nonclassical algebraic solution of a 3-variable irregular Garnier system is constructed. Diarra--Loray have studied classification of algebraic solutions of irregular Garnier systems. There are two type of the algebraic solutions: classical type and pull-back type. They have shown that there are exactly three nonclassical algebraic solutions

Parinya Chalermsook, Chien-Chung Huang, Danupon Nanongkai, Thatchaphol Saranurak

In the $k$-edge-connected spanning subgraph ($k$ECSS) problem, our goal is to compute a minimum-cost sub-network that is resilient against up to $k$ link failures: Given an $n$-node $m$-edge graph with a cost function on the edges, our goal is to compute a minimum-cost $k$-edge-connected spanning subgraph. This NP-hard problem generalizes the minimum spannin

Aviv A. Rosenberg, Sanketh Vedula, Yaniv Romano, Alex M. Bronstein

Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable $\mathrm{Y}$ given explanatory features $\boldsymbol{\mathrm{X}}$. A limitation of QR is that it is only defined for scalar target variables, due to the formulation of its objective function, and since the notion of quantiles has no standard defi

Hanqi Wang, Xiaoguang Zhu, Tao Chen, Chengfang Li

Deep learning is widely used to decode the electroencephalogram (EEG) signal. However, there are few attempts to specifically investigate how to explain the EEG-based deep learning models. We conduct a review to summarize the existing works explaining the EEG-based deep learning model. Unfortunately, we find that there is no appropriate method to explain the

Rebecca K. Webb, Siddharth Gandhi, Matteo Brogi, Jayne L. Birkby

Characterising the atmospheres of hot Jupiters is important in understanding the formation and migration of these exotic planets. However, there are still many open questions about the chemical and physical properties of these atmospheres. Here, we confirm the detection of water vapour in thermal emission from the non-transiting hot Jupiter {\tau} Bootis Ab

Rhombik Roy, Barnali Chakrabarti, N. D. Chavda, M. L. Lekala

The present work reports on the dynamical measures of order, disorder and complexity for the interacting bosons in optical lattice. We report results both for the relaxed state as well as quench dynamics. Our key observations are: (1) Lattice depth can be taken as order-disorder parameter. (2) The superfluid to Mott insulator transition can be treated as `or

N. Dimakis

It is well established that the mass parameter breaks the conformal symmetries in the case of geodesic motion. The proper conformal Killing vectors cease to generate conserved charges when non-null geodesics are considered. We examine how the introduction of the mass is actually related to the appearance of appropriate distortions in the conformal sector, wh

Marie-Charlotte Brandenburg, Georg Loho, Rainer Sinn

We initiate the study of positive-tropical generators as positive analogues of the concept of tropical bases. Applying this to the tropicalization of determinantal varieties, we develop criteria for characterizing their positive part. We focus on the study of low-rank matrices, in particular matrices of rank 2 and 3. Moreover, in the case square-matrices of

Shuxuan Guo, Yinlin Hu, Jose M. Alvarez, Mathieu Salzmann

Knowledge distillation facilitates the training of a compact student network by using a deep teacher one. While this has achieved great success in many tasks, it remains completely unstudied for image-based 6D object pose estimation. In this work, we introduce the first knowledge distillation method driven by the 6D pose estimation task. To this end, we obse

Penghui Wei, Shaoguo Liu, Xuanhua Yang, Liang Wang

Current bundle generation studies focus on generating a combination of items to improve user experience. In real-world applications, there is also a great need to produce bundle creatives that consist of mixture types of objects (e.g., items, slogans and templates) for achieving better promotion effect. We study a new problem named bundle creative generation

Jinyi Wang, Zhaoyang Lyu, Dahua Lin, Bo Dai

With wider application of deep neural networks (DNNs) in various algorithms and frameworks, security threats have become one of the concerns. Adversarial attacks disturb DNN-based image classifiers, in which attackers can intentionally add imperceptible adversarial perturbations on input images to fool the classifiers. In this paper, we propose a novel purif

Xiaopeng Zheng, Hao Wang, Peng Zheng, Zhihui Qi

Quadrilateral meshes with high level structure and feature preserving property benefit industrial applications the most. Generation of such quad mesh remains a challenge. Quad meshes generated using surface foliation have the highest level structure, however they lack of the feature preserving ability. In this paper, we analyze the boundary curvature with Ga

Mohsen Ghaffari, Goran Zuzic

We present a universally-optimal distributed algorithm for the exact weighted min-cut. The algorithm is guaranteed to complete in $\widetilde{O}(D + \sqrt{n})$ rounds on every graph, recovering the recent result of Dory, Efron, Mukhopadhyay, and Nanongkai~[STOC'21], but runs much faster on structured graphs. Specifically, the algorithm completes in $\widetil

Anzor Khelashvili, Teimuraz Nadareishvili

The problem of boundary behaviour at the origin of coordinates is discussed for D-dimensional Schrodinger equation in the framework of hyper spherical formalism, which have been often considered last time. We show that the Dirichlet condition, which seems as natural, is not mathematically well justified, on the contrary to the 3-dimensional case. The stronge

Sujata Bhowmick, Sethupathy Subramanian

The phenomenon of linear motion of conductor in a magnetic field is commonly found in electric machineries such as, electromagnetic brakes, linear induction motor, electromagnetic flowmeter etc. The design and analysis of the same requires an accurate evaluation of induced currents and the associated reaction magnetic fields. The finite element method is a g

Luyang Li, Ligang He, Jinjin Gao, Xie Han

In order to retain more feature information of local areas on a point cloud, local grouping and subsampling are the necessary data structuring steps in most hierarchical deep learning models. Due to the disorder nature of the points in a point cloud, the significant time cost may be consumed when grouping and subsampling the points, which consequently result

Thijs Klooster, Fatih Turkmen, Gerben Broenink, Ruben ten Hove

Fuzzing has proven to be a fundamental technique to automated software testing but also a costly one. With the increased adoption of CI/CD practices in software development, a natural question to ask is `What are the best ways to integrate fuzzing into CI/CD pipelines considering the velocity in code changes and the automated delivery/deployment practices?'.

K. Giers, S. Spezzano, F. Alves, P. Caselli

Context: In the centre of pre-stellar cores, the deuterium fractionation is enhanced due to the cold temperatures and high densities. Therefore, the chemistry of deuterated molecules can be used to probe the evolution and the kinematics in the earliest stages of star formation. Aims: We analyse emission maps of cyclopropenylidene, c-C$_3$H$_2$, to study the

Nicholas Gao, Stephan Günnemann

Recently, it has been shown that neural networks not only approximate the ground-state wave functions of a single molecular system well but can also generalize to multiple geometries. While such generalization significantly speeds up training, each energy evaluation still requires Monte Carlo integration which limits the evaluation to a few geometries. In th

Nikolay Moshchevitin

We discuss some easy statements dealing with linear inhomogeneous Diophantine approximation. Surprisingly, we did not find some of them in the literature.

Haley Hoech, Roman Rischke, Karsten Müller, Wojciech Samek

Federated learning suffers in the case of non-iid local datasets, i.e., when the distributions of the clients' data are heterogeneous. One promising approach to this challenge is the recently proposed method FedAUX, an augmentation of federated distillation with robust results on even highly heterogeneous client data. FedAUX is a partially $(\epsilon, \delta

Mounia Hamidouche, Reda Bellafqira, Gwenolé Quellec, Gouenou Coatrieux

The advances in machine learning (ML) have greatly improved AI-based diagnosis aid systems in medical imaging. However, being based on collecting medical data specific to individuals induces several security issues, especially in terms of privacy. Even though the owner of the images like a hospital put in place strict privacy protection provisions at the lev

Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun

The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning. Recent studies on dataset condensation attempt to reduce the dependence on such massive data by synthesizing a compact training dataset. However, the existing approaches have fundamental limitations in optimizatio

Ibrahim Atayev, Shahin Mamedov

We study the axial-vector form factor of the nucleons at constant and homogenous isospin chemical potential using holographic QCD. Nucleon mass splitting in such isospin medium is taken into account. According to AdS/CFT correspondence, the critical value of the isospin chemical potential \mu_I equals to UV- boundary value of the time component of the bulk g

She-Sheng Xue

The CDF collaboration's recent high-precision measurement of the $W$mass is in $7.0~\sigma$ disagreement with the Standard Model expectation. This tension will be relieved if the $W$ boson has a non-trivial right-handed gauge coupling at high energies. At TeV scales, the SM gauge symmetric four-fermion interactions induce a right-handed gauge coupling, and S

Hua-Ying Liu, Minghao Shang, Xiaoyi Liu, Ying Wei

Large-photon-number quantum state is a fundamental but non-resolved request for practical quantum information applications. Here we propose an N-photon state generation scheme that is feasible and scalable, using lithium niobate on insulator circuits. Such scheme is based on the integration of a common building block called photon-number doubling unit (PDU),

Yulin Shao, Emre Ozfatura, Alberto Perotti, Branislav Popovic

Ultra-reliable short-packet communication is a major challenge in future wireless networks with critical applications. To achieve ultra-reliable communications beyond 99.999%, this paper envisions a new interaction-based communication paradigm that exploits feedback from the receiver. We present AttentionCode, a new class of feedback codes leveraging deep le

A. Letourneau, V. Savu, D. Lhuillier, Th. Lasserre

We investigate the possible origins of the norm and shape reactor antineutrino anomalies in the framework of a summation model (SM) where $\beta^{-}$ transitions are simulated by a phenomenological Gamow-Teller $\beta$-decay strength model. The general trends of the discrepancies to the Huber-Mueller model on the antineutrino side can be reproduced both in n